Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm
One of the most complex and life-threatening pathologies of the central nervous system is brain tumors. Correct diagnosis of these tumors plays an important role in determining the treatment plans of patients. Traditional classification methods often rely on manual assessments, which can be prone to...
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2556.pdf |
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| author | Kerem Gencer Gülcan Gencer |
| author_facet | Kerem Gencer Gülcan Gencer |
| author_sort | Kerem Gencer |
| collection | DOAJ |
| description | One of the most complex and life-threatening pathologies of the central nervous system is brain tumors. Correct diagnosis of these tumors plays an important role in determining the treatment plans of patients. Traditional classification methods often rely on manual assessments, which can be prone to error. Therefore, multiple classification of brain tumors has gained significant interest in recent years in both the medical and computer science fields. The use of artificial intelligence and machine learning, especially in the automatic classification of brain tumors, is increasing significantly. Deep learning models can achieve high accuracy when trained on datasets in diagnosis and classification. This study examined deep learning-based approaches for automatic multi-class classification of brain tumors, and a new approach combining deep learning and quantum genetic algorithms (QGA) was proposed. The powerful feature extraction ability of the pre-trained EfficientNetB0 was utilized and combined with this quantum genetic algorithms, a new approach was proposed. It is aimed to develop the feature selection method. With this hybrid method, high reliability and accuracy in brain tumor classification was achieved. The proposed model achieved high accuracy of 98.36% and 98.25%, respectively, with different data sets and significantly outperformed traditional methods. As a result, the proposed method offers a robust and scalable solution that will help classify brain tumors in early and accurate diagnosis and contribute to the field of medical imaging with patient outcomes. |
| format | Article |
| id | doaj-art-bf886d90eed64b2b962b44269051f4cb |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ Computer Science |
| spelling | doaj-art-bf886d90eed64b2b962b44269051f4cb2025-01-23T15:05:09ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e255610.7717/peerj-cs.2556Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithmKerem Gencer0Gülcan Gencer1Afyon Kocatepe University, Faculty of Engineering, Department of Computer Engineering, Afyonkarahisar, TurkeyAfyonkarahisar Health Sciences University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Afyonkarahisar, TurkeyOne of the most complex and life-threatening pathologies of the central nervous system is brain tumors. Correct diagnosis of these tumors plays an important role in determining the treatment plans of patients. Traditional classification methods often rely on manual assessments, which can be prone to error. Therefore, multiple classification of brain tumors has gained significant interest in recent years in both the medical and computer science fields. The use of artificial intelligence and machine learning, especially in the automatic classification of brain tumors, is increasing significantly. Deep learning models can achieve high accuracy when trained on datasets in diagnosis and classification. This study examined deep learning-based approaches for automatic multi-class classification of brain tumors, and a new approach combining deep learning and quantum genetic algorithms (QGA) was proposed. The powerful feature extraction ability of the pre-trained EfficientNetB0 was utilized and combined with this quantum genetic algorithms, a new approach was proposed. It is aimed to develop the feature selection method. With this hybrid method, high reliability and accuracy in brain tumor classification was achieved. The proposed model achieved high accuracy of 98.36% and 98.25%, respectively, with different data sets and significantly outperformed traditional methods. As a result, the proposed method offers a robust and scalable solution that will help classify brain tumors in early and accurate diagnosis and contribute to the field of medical imaging with patient outcomes.https://peerj.com/articles/cs-2556.pdfBrain tumorDeep learningConvolutional neural networkQuantum genetic algorithmsMedical image analysis |
| spellingShingle | Kerem Gencer Gülcan Gencer Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm PeerJ Computer Science Brain tumor Deep learning Convolutional neural network Quantum genetic algorithms Medical image analysis |
| title | Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm |
| title_full | Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm |
| title_fullStr | Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm |
| title_full_unstemmed | Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm |
| title_short | Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm |
| title_sort | hybrid deep learning approach for brain tumor classification using efficientnetb0 and novel quantum genetic algorithm |
| topic | Brain tumor Deep learning Convolutional neural network Quantum genetic algorithms Medical image analysis |
| url | https://peerj.com/articles/cs-2556.pdf |
| work_keys_str_mv | AT keremgencer hybriddeeplearningapproachforbraintumorclassificationusingefficientnetb0andnovelquantumgeneticalgorithm AT gulcangencer hybriddeeplearningapproachforbraintumorclassificationusingefficientnetb0andnovelquantumgeneticalgorithm |